PROJECT SUMMARY Chronic Kidney Disease (CKD) is a major disease multiplier in patients aged 65+. CKD is characterized by progressive renal fibrosis mediated through supraphysiologic type IV collagen deposition by renal myofibroblasts. As the US population continues to age, it becomes increasingly critical to identify new therapeutic strategies for CKD. Mouse models of kidney injury suggest reducing the activity of the receptor tyrosine kinase discoidin domain receptor 1 (DDR1) is protective against fibrotic renal disease. Inhibition of DDR1 kinase reduces mesangial cell deposition of type IV collagen. To develop targeted therapeutics for CKD, the laboratory of Jens Meiler (sponsor of this application) partners with the laboratories of Ambra Pozzi (co- sponsor of this application) and Craig Lindsley to create a comprehensive DDR1 kinase inhibitor discovery pipeline. The Meiler laboratory utilizes a combination of ligand-based quantitative structure-activity relationship (QSAR) modeling for virtual high-throughput screening (vHTS) and subsequent protein-ligand docking to identify lead compounds for synthesis/derivatization (Lindsley) and biochemical/functional evaluation (Pozzi). Selective targeting of individual kinases remains a significant challenge, and current methods in vHTS fail to account for protein binding pocket features contributing to binding selectivity. The central objectives of this proposal are to identify novel DDR1-selective inhibitors for the treatment of CKD and to develop new technologies to address current limitations in vHTS. In Specific Aim I, I will generate and use QSAR models to perform vHTS for potential DDR1 inhibitors. I will subsequently define a structural model of DDR1 kinase inhibitor selectivity using molecular dynamics (MD)-generated conformational ensembles of DDR kinases in conjunction with ROSETTA flexible docking. I will also perform in silico and in vitro site-directed mutagenesis to further characterize the determinants of DDR1 kinase inhibitor selectivity. In Specific Aim II, I will develop a multitasking machine algorithm within the Meiler lab BIOLOGY AND CHEMISTRY LIBRARY (BCL) which will leverage protein structural information in addition to conventional ligand-based descriptors to improve vHTS for selective DDR1 kinase inhibitors. The methods developed will address long-standing shortcomings in the field of computer-aided drug discovery (CADD) ? namely, that protein structure-based methods are computationally prohibitive for vHTS while ligand-based methods do not include direct information on binding mode. As the methods developed in Aim II become available, they will be integrated in the discovery cycle described in Aim I to ultimately define a structural model of DDR1 kinase selectivity and identify novel therapeutic agents for the treatment of CKD through the use of new and established methods. Furthermore, novel computational methods established in these studies will be broadly applicable to other challenging targets in drug discovery.